PV-RCNN

An unofficial Pytorch implementation of PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection.

News (03/02/2020)

• Added implementation of SECOND.

Project goals

• Emphasis on simple codebase (no 1,000 LOC functions).
• General 3D detection library (easy to extend to new models).
• Hope to reproduce results of paper.

Status and plans

• This repo is still under active development.
• I will post a pretrained model when codebase stabilizes and results are good.
• I will add more detailed training and inference instructions.
• I will add description of codebase and design choices.

Usage

See inference.py.

Installation

See install.md and please ask if you have any questions. I will supply a Docker build soon.

Citing

If you find this work helpful in your research, please consider starring this repo and citing:

@article{pvrcnnpytorch,
  author={Jacob Hultman},
  title={PV-RCNN PyTorch},
  journal={https://github.com/jhultman/PV-RCNN},
  year={2020}
}

and the original PV-RCNN paper (note I am not an author of this paper):

@article{shi2019pv,
  author={Shi, Shaoshuai and Guo, Chaoxu and Jiang, Li and Wang, Zhe and Shi, Jianping and Wang, Xiaogang and Li, Hongsheng},
  title={PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection},
  journal={arXiv preprint arXiv:1912.13192},
  year={2019}
}

Contributions

Contributions are welcome. Please post an issue if you find any bugs.

Acknowledgements and licensing

Please see license.md. Note that the code in pvrcnn/ops is largely from detectron2 and hence is subject to the Apache license. Thank you to the authors of PV-RCNN for their research.